Medical Geography: a Promising Field of Application for Geostatistics.
Medical Geography: a Promising Field of Application for Geostatistics.
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DOI：
10.1007/s11004-008-9211-3
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发表时间：
2009
影响因子：
2.6
通讯作者：
Goovaerts, P.
中科院分区：
文献类型：
--
作者：
Goovaerts, P.
对健康数据和假定协变量（如环境、社会经济、行为或人口因素）的分析是地统计学的一个有前途的应用。然而，它提出了一些方法上的挑战，因为数据通常是在不规则的空间支持下汇总的，由分子和分母（即人口规模）组成。本文概述了卫生地质统计学领域的最新发展，重点是区域卫生数据分析的三个主要步骤：估计潜在疾病风险，检测风险明显较高的地区，以及分析与假定风险因素的关系。该分析说明了使用年龄调整的宫颈癌死亡率记录在1970-1994年期间，在美国西部的四个州的118个县。泊松克里格允许过滤噪音的死亡率计算从小的人口规模，增强与两个假定的解释变量的相关性：居住在联邦定义的贫困线以下的居民的百分比，和西班牙裔女性的百分比。面到点克里金公式创建连续的死亡率风险图，减少与分区图解释相关的视觉偏差。随机模拟用于生成癌症死亡率地图的实现，这使得人们能够在数字上量化健康结果的空间分布的不确定性如何转化为高值集群位置或与协变量相关性的不确定性。最后，地理加权回归突出了协变量解释力的非平稳性：与犹他州记录的较低风险相比，两个协变量更好地解释了沿着海岸的较高死亡率值。
The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. It presents, however, several methodological challenges that arise from the fact that data are typically aggregated over irregular spatial supports and consist of a numerator and a denominator (i.e. population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: estimation of the underlying disease risk, detection of areas with significantly higher risk, and analysis of relationships with putative risk factors. The analysis is illustrated using age-adjusted cervix cancer mortality rates recorded over the 1970–1994 period for 118 counties of four states in the Western USA. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlation with two putative explanatory variables: percentage of habitants living below the federally defined poverty line, and percentage of Hispanic females. Area-to-point kriging formulation creates continuous maps of mortality risk, reducing the visual bias associated with the interpretation of choropleth maps. Stochastic simulation is used to generate realizations of cancer mortality maps, which allows one to quantify numerically how the uncertainty about the spatial distribution of health outcomes translates into uncertainty about the location of clusters of high values or the correlation with covariates. Last, geographically-weighted regression highlights the non-stationarity in the explanatory power of covariates: the higher mortality values along the coast are better explained by the two covariates than the lower risk recorded in Utah.
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影响因子：
4.9
作者：
Avruskin GA;Jacquez GM;Meliker JR;Slotnick MJ;Kaufmann AM;Nriagu JO
通讯作者：
Nriagu JO
DOI：
10.1198/106186007x179257
发表时间：
2007-03-01
影响因子：
2.4
作者：
Gotway, Carol A.;Young, Linda J.
通讯作者：
Young, Linda J.
DOI：
10.1093/jnci/84.13.1030
发表时间：
1992-07-01
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
FRIEDELL, GH;TUCKER, TC;NADEL, M
通讯作者：
NADEL, M
影响因子：
3.6
作者：
ANSELIN, L
通讯作者：
ANSELIN, L
影响因子：
4.9
作者：
Goovaerts, Pierre;Jacquez, Geoffrey M
通讯作者：
Jacquez, Geoffrey M